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関連する概念動画

Introduction to Learning01:18

Introduction to Learning

Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
In contrast to learned behaviors, unlearned behaviors such as crying, sexual...
Cognitive Learning01:21

Cognitive Learning

Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
E. C. Tolman's theory of purposive behavior emphasizes that much behavior is goal-directed. He argued that to understand behavior, we must look at the entire sequence of actions leading to a goal. For instance, high school students study hard, not just due to past reinforcement but also to achieve the goal of getting into a good college.
Tolman introduced the idea that behavior is influenced by...
Purposive Learning01:22

Purposive Learning

E. C. Tolman emphasized the purposiveness of behavior — the idea that much of our behavior is goal-directed. For instance, employees who aim for a promotion work diligently to meet their targets. Tolman argued that when classical conditioning and operant conditioning occur, the organism acquires certain expectations. In classical conditioning, a child might fear a dog because they expect it to bite. In operant conditioning, a person might consistently work overtime because they expect a bonus...
Observational Learning01:12

Observational Learning

Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning because...
Language Development01:22

Language Development

Children master language quickly and with relative ease, supported by both biological predisposition and reinforcement. B. F. Skinner (1957) proposed that language is learned through reinforcement, while Noam Chomsky (1965) argued that language acquisition mechanisms are biologically determined.
The critical period for language acquisition suggests that the ability to acquire language is at its peak early in life. As people age, this proficiency decreases. Language development begins very...
Modeling in Therapy01:26

Modeling in Therapy

Modeling, a key technique in therapy, uses observational learning to help clients acquire and practice new skills by watching therapists demonstrate desired behaviors. This approach, rooted in Albert Bandura's concept of vicarious learning, plays a significant role in therapeutic interventions for various psychological conditions, including social anxiety, ADHD, and depression.
Participant Modeling
Participant modeling involves therapists demonstrating calm and effective behaviors in situations...

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Updated: Jun 27, 2026

Experimental Paradigm for Measuring the Effect of Induced Emotion on Grammar Learning
05:33

Experimental Paradigm for Measuring the Effect of Induced Emotion on Grammar Learning

Published on: January 29, 2020

学習フェアの代表は,事前に訓練された言語モデルを微調整するための学習フェアです.

Ke Wang1, Yinghao Zhang1, Hong-Yu Zhang1

  • 1College of Informatics, Huazhong Agricultural University, Wuhan, Hubei, 430070, China.

Neural networks : the official journal of the International Neural Network Society
|February 19, 2026
PubMed
まとめ
この要約は機械生成です。

この研究は,事前訓練された言語モデル (PLM) のデバイジングのための新しい枠組みであるCFPLMを導入します. CFPLMは,AI言語モデルの社会的バイアスを性能を損なうことなく減らすために因果推論を使用しています.

キーワード:
原因推論による因果推論公平さ 公平さ 公平さ前もって訓練された言語モデル.

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Last Updated: Jun 27, 2026

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Published on: January 29, 2020

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科学分野:

  • 人工知能 (AI) とは,人工知能 (AI) のことです.
  • 自然言語処理 (Natural Language Processing) とは,自然言語処理で処理される言語のことです.
  • 機械学習 (Machine Learning) とは,機械学習 (Machine Learning) について学ぶことです.

背景:

  • 先行訓練された言語モデル (PLM) は,様々なNLPタスクに優れているが,人間のバイアスを継承している.
  • 社会的ステレオタイプを含むこれらのバイアスは,PLMの安全で倫理的な適用を制限します.
  • 既存のデバイスングの方法は,バイアスの根本的な原因を効果的に取り除くのに失敗することが多い.

研究 の 目的:

  • 前もって訓練された言語モデルのための新しいデビージングフレームワーク,CFPLMを提案する.
  • PLM内のバイアスを誘発する要因を特定し,介入するために因果推論を活用する.
  • 言語理解能力を維持しながら,PLMの公平性を高める.

主な方法:

  • CFPLM (事前訓練された言語モデルのための因果的枠組み) のデバイジングフレームワークを開発しました.
  • 整合的な損失関数と公平性のペナルティ条件を組み込みました.
  • 性能最適化のための対抗的な損失とエントロピーの正規化を統合した.

主要な成果:

  • CFPLMは,BERT,RoBERTa,ALBERTのような人気のあるPLMのバイアスを大幅に削減しました.
  • 標準データセットとメトリックに関する評価は,デビアージングアプローチの有効性を確認しました.
  • GLUEベンチマークでのパフォーマンスは,言語理解能力の妥協を示さなかった.

結論:

  • 提案されたCFPLMフレームワークは,因果推論を使用してPLMのバイアスを効果的に軽減します.
  • CFPLMによる公平性の強化は,モデルのコア言語理解能力に悪影響を及ぼさない.
  • CFPLMは,より倫理的で信頼性の高いAI言語技術を開発するための有望な方向性を提供します.